Monetary Policy Rules in the BRICS: How Important is Nonlinearity?

Given limited research on monetary policy rules in emerging markets, this paper estimates monetary policy rules for five key emerging market economies: Brazil, Russia, India, China and South Africa (BRICS) analysing whether the monetary authority reacts to changes in financial markets, in monetary conditions, in the foreign exchange sector and in the commodity price. To get a deeper understanding of the central bank’s behaviour, we assess the importance of nonlinearity using a smooth transition (STAR) model. Using quarterly data, we find strong evidence that the monetary policy followed by the Central Banks in the BRICS varies from one country to another and that it exhibits nonlinearity. In particular, considerations about economic growth (in the cases of Brazil and Russia), inflation (for India and China) and stability of financial markets (in South Africa) seem to be the major drivers of such nonlinear monetary policy behaviour. Moreover, the findings suggest that the monetary authorities pursue, with the exception of India, a target range for the threshold variable rather than a specific point target. In fact, the exponential smooth transition regression (ESTR) model seems to be the best description of the monetary policy rule in these countries.monetary policy, emerging markets, smooth transition.

Fiscal Policy in the BRICs

This paper assesses the macroeconomic impact of fiscal policy shocks for four key emerging market economies - Brazil, Russia, India and China (BRICs) – using a Bayesian Structural Vector Auto-Regressive (BSVAR) approach, a Sign-Restrictions Vector Auto-Regressive framework and a Panel Vector Auto-Regressive (PVAR) model. To get a deeper understanding of the government’s behaviour, we also estimate fiscal policy rules using a Fully Simultaneous System of Equations and analyze the importance of nonlinearity using a smooth transition (STAR) model. Drawing on quarterly frequency data, we find that government spending shocks have strong Keynesian effects for this group of countries while, in the case of government revenue shocks, a tax hike is harmful for output. This suggests that there is no evidence in favour of ‘expansionary fiscal contraction’ in the context of emerging economies where spending policies are largely pro-cyclical. Our findings also show that considerations about growth (in the case of China), exchange rate and inflation (for Brazil and Russia) and commodity prices (in India) drive the nonlinear response of fiscal policy to the dynamics of the economy. All in all, our results are consistent with the idea that fiscal policy can be a powerful stabilization tool and can provide an important short-term economic boost for emerging markets, in particular, in the context of severe downturns as in most recent financial turmoil.fiscal policy, emerging markets, fully simultaneous system of equations, sign-restrictions VAR, smooth transition regression model

Monetary policy rules in the BRICS : how important is nonlinearity?

Given limited research on monetary policy rules in emerging markets, this paper estimates monetary policy rules for five key emerging market economies: Brazil, Russia, India, China and South Africa (BRICS) analysing whether the monetary authority reacts to changes in financial markets, in monetary conditions, in the foreign exchange sector and in the commodity price. To get a deeper understanding of the central bank’s behaviour, we assess the importance of nonlinearity using a smooth transition (STAR) model. Using quarterly data, we find strong evidence that the monetary policy followed by the Central Banks in the BRICS varies from one country to another and that it exhibits nonlinearity. In particular, considerations about economic growth (in the cases of Brazil and Russia), inflation (for India and China) and stability of financial markets (in South Africa) seem to be the major drivers of such nonlinear monetary policy behaviour. Moreover, the findings suggest that the monetary authorities pursue, with the exception of India, a target range for the threshold variable rather than a specific point target. In fact, the exponential smooth transition regression (ESTR) model seems to be the best description of the monetary policy rule in these countries.Fundação para a Ciência e a Tecnologia (FCT

Fiscal policy in the BRICs

This paper assesses the macroeconomic impact of fiscal policy shocks for four key emerging market economies - Brazil, Russia, India and China (BRICs) – using a Bayesian Structural Vector Auto-Regressive (BSVAR) approach, a Sign-Restrictions Vector Auto-Regressive framework and a Panel Vector Auto-Regressive (PVAR) model. To get a deeper understanding of the government’s behaviour, we also estimate fiscal policy rules using a Fully Simultaneous System of Equations and analyze the mportance of nonlinearity using a smooth transition (STAR) model. Drawing on quarterly frequency data, we find that government spending shocks have strong Keynesian effects for this group of countries while, in the case of government revenue shocks, a tax hike is harmful for output. This suggests that there is no evidence in favour of ‘expansionary fiscal contraction’ in the context of emerging economies where spending policies are largely pro-cyclical. Our findings also show that considerations about growth (in the case of China), exchange rate and inflation (for Brazil and Russia) and commodity prices (in India) drive the nonlinear response of fiscal policy to the dynamics of the economy. All in all, our results are consistent with the idea that fiscal policy can be a powerful stabilization tool and can provide an important short-term economic boost for emerging markets, in particular, in the context of severe downturns as in most recent financial turmoil.Fundação para a Ciência e a Tecnologia (FCT

Radiative signature of absorbing aerosol over the eastern Mediterranean basin

The effects of absorbing aerosols on the atmospheric radiation budget and dynamics over the eastern Mediterranean region are studied using satellites and ground-based observations, and radiative transfer model calculations, under summer conditions. Climatology of aerosol optical depth (AOD), single scattering albedo (SSA) and size parameters were analyzed using multi-year (1999–2012) observations from Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR) and AErosol RObotic NETwork (AERONET). Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)-derived aerosol vertical distributions and their classifications are used to calculate the AOD of four dominant aerosol types: dust, polluted dust, polluted continental, and marine aerosol over the region. The seasonal mean (June–August 2010) AODs are 0.22 ± 0.02, 0.11 ± 0.04, 0.10 ± 0.04 and 0.06 ± 0.01 for polluted dust, polluted continental, dust and marine aerosol, respectively. Changes in the atmospheric temperature profile as a function of absorbing aerosol loading were derived for the same period using observations from the AIRS satellite. We inferred heating rates in the aerosol layer of ~1.7 ± 0.8 K day−1 between 925 and 850 hPa, which is attributed to aerosol absorption of incoming solar radiation. Radiative transfer model (RTM) calculations show significant atmospheric warming for dominant absorbing aerosol over the region. A maximum atmospheric forcing of +16.7 ± 7.9 Wm−2 is calculated in the case of polluted dust, followed by dust (+9.4 ± 4.9 Wm&minus;2) and polluted continental (+6.4 ± 4.5 Wm&minus;2). RTM-derived heating rate profiles for dominant absorbing aerosol show warming of 0.1–0.9 K day−1 in the aerosol layer (< 3.0 km altitudes), which primarily depend on AODs of the different aerosol types. Diabatic heating due to absorbing aerosol stabilizes the lower atmosphere, which could significantly reduce the atmospheric ventilation. These conditions can enhance the "pollution pool" over the eastern Mediterranean

Deforestation: Correlations, Possible Causes and Some Implications

Changes in national forest areas during 1990-2000 are contrasted with other variables to illustrate correlations and provoke discussion about possible causes. Twenty-five statistically-significant correlations (including rural population, life expectancy, GDP, literacy, commerce, agriculture, poverty and inflation) are illustrated and a statistical model suggests that good governance, alternative employment opportunities, and payments for environmental services may be effective in combating deforestation. The data suggest that a global forest convention may need to be supported by substantial and carefully-targeted development assistance to foster good governance

Molecular analysis of HBV genotypes and subgenotypes in the Central-East region of Tunisia

<p>Abstract</p> <p>Background</p> <p>In Tunisia, country of intermediate endemicity for Hepatitis B virus (HBV) infection, most molecular studies on the virus have been carried out in the North of the country and little is known about other regions. The aim of this study was to determine HBV genotype and subgenotypes in Central-East Tunisia. A total of 217 HBs antigen positive patients were enrolled and determination of genotype was investigated in 130 patients with detectable HBV DNA. HBV genotyping methods were: PCR-RFLP on the pre-S region, a PCR using type-specific primers in the S region (TSP-PCR) and partial sequencing in the pre-S region.</p> <p>Results</p> <p>Three genotypes (D, B and A) were detected by the PCR-RFLP method and two (D and A) with the TSP-PCR method, the concordance between the two methods was 93%. Sequencing and phylogenetic analysis of 32 strains, retrieved the same genotype (D and A) for samples with concordant results and genotype D for samples with discordant results. The sequences of discordant genotypes had a restriction site in the pre-S gene which led to erroneous result by the PCR-RFLP method. Thus, prevalence of genotype D and A was 96% and 4%, respectively. Phylogenetic analysis showed the predominance of two subgenotypes D1 (55%) and D7 (41%). Only one strain clustered with D3 subgenotype (3%).</p> <p>Conclusions</p> <p>Predominance of subgenotype D7 appears to occur in northern regions of Africa with transition to subgenotype D1 in the East of the continent. HBV genetic variability may lead to wrong results in rapid genotyping methods and sequence analysis is needed to clarify atypical results.</p

Formation of the ηc\eta_c in Two-Photon Collisions at LEP

The two-photon width $\Gamma_{\gamma\gamma}$ of the $\eta_c$ meson has been measured with the L3 detector at LEP. The $\eta_c$ is studied in the decay modes $\pi^+\pi^-\pi^+\pi^-$, $\pi^+\pi^-$K$^+$K$^-$, K$_s^0$K$^\pm\pi^\mp$, K$^+$K$^-\pi^{0}$, $\pi^+\pi^-\eta$, $\pi^+\pi^-\eta'$, and $\rho^+\rho^-$ using an integrated luminosity of 140 pb$^{-1}$ at $\sqrt{s} \simeq 91$ GeV and of 52 pb$^{-1}$ at $\sqrt{s} \simeq 183$ GeV. The result is $\Gamma_{\gamma\gamma}(\eta_c) = 6.9 \pm 1.7 (stat.) \pm 0.8 (sys.) \pm 2.0$(BR) keV. The $Q^2$ dependence of the $\eta_c$ cross section is studied for $Q^2 < 9$ GeV$^{2}$. It is found to be better described by a Vector Meson Dominance model form factor with a J-pole than with a $\rho$-pole. In addition, a signal of $29 \pm 11$ events is observed at the $\chi_c0$ mass. Upper limits for the two-photon widths of the $\chi_c0$, $\chi_c2$, and $\eta_c'$ are also given

Search for Charginos with a Small Mass Difference with the Lightest Supersymmetric Particle at \sqrt{s} = 189 GeV

A search for charginos nearly mass-degenerate with the lightest supersymmetric particle is performed using the 176 pb^-1 of data collected at 189 GeV in 1998 with the L3 detector. Mass differences between the chargino and the lightest supersymmetric particle below 4 GeV are considered. The presence of a high transverse momentum photon is required to single out the signal from the photon-photon interaction background. No evidence for charginos is found and upper limits on the cross section for chargino pair production are set. For the first time, in the case of heavy scalar leptons, chargino mass limits are obtained for any \tilde{\chi}^{+-}_1 - \tilde{\chi}^0_1 mass difference